Point'n Move：基于高斯泼溅辐射场的交互式场景物体操控
Point'n Move: Interactive Scene Object Manipulation on Gaussian Splatting Radiance Fields
AI总结：
Point'n Move利用高斯泼溅辐射场的显式表示，通过双阶段自提示分割和实时修复实现直观、高质量的交互式场景物体操控与移除。
摘要：
我们提出 Point'n Move，一种实现交互式场景物体操控并具备暴露区域修复能力的方法。此处的交互性进一步来源于直观的物体选择与实时编辑。为实现这一目标，我们采用高斯泼溅辐射场（Gaussian Splatting Radiance Field）作为场景表示，并充分利用其显式特性和速度优势。其显式表示形式使我们能够设计一种从二维提示点到三维掩码的双阶段自提示分割算法，执行掩码细化与合并，最小化改动，同时为场景修复提供良好的初始化，并无需针对每次编辑进行训练即可实时执行编辑，所有这些都带来了更高的质量和性能。我们通过在面向前的场景和360度场景上执行编辑来测试我们的方法。我们还将我们的方法与现有的场景物体移除方法进行了比较，结果显示，尽管我们的方法能力更强且具有速度优势，仍展现出更优的质量。
英文摘要：
We propose Point'n Move, a method that achieves interactive scene object manipulation with exposed region inpainting. Interactivity here further comes from intuitive object selection and real-time editing. To achieve this, we adopt Gaussian Splatting Radiance Field as the scene representation and fully leverage its explicit nature and speed advantage. Its explicit representation formulation allows us to devise a 2D prompt points to 3D mask dual-stage self-prompting segmentation algorithm, perform mask refinement and merging, minimize change as well as provide good initialization for scene inpainting and perform editing in real-time without per-editing training, all leads to superior quality and performance. We test our method by performing editing on both forward-facing and 360 scenes. We also compare our method against existing scene object removal methods, showing superior quality despite being more capable and having a speed advantage.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
